1. Field of the Disclosure
The present disclosure relates generally to artificial neuron networks and more particularly in one exemplary aspect to computerized apparatus and methods for encoding sensory input using spiking neuron networks.
2. Description of Related Art
Artificial spiking neural networks are frequently used to gain an understanding of biological neural networks, and for solving artificial intelligence problems. These networks typically employ a pulse-coded mechanism, which encodes information using timing of the pulses. Such pulses (also referred to as “spikes” or ‘impulses’) are short-lasting discrete temporal events, typically on the order of 1-2 milliseconds (ms). Several exemplary embodiments of such encoding are described in a commonly owned and co-pending U.S. patent application Ser. No. 13/152,084 entitled “APPARATUS AND METHODS FOR PULSE-CODE INVARIANT OBJECT RECOGNITION”, filed Jun. 2, 2011, and co-owned U.S. patent application Ser. No. 13/152,119, filed Jun. 2, 2011, entitled “SENSORY INPUT PROCESSING APPARATUS AND METHODS”, issued as U.S. Pat. No. 8,942,466 on Jan. 27, 2015, each incorporated herein by reference in its entirety.
A typical artificial spiking neural network, may comprise a plurality of units (or nodes), which may correspond to neurons in a biological neural network. A given unit may be connected to one (or more) other units via connections, also referred to as communication channels, or synaptic connections. The units providing inputs to a given unit may be referred to as the pre-synaptic units, while the unit receiving the inputs may be referred to as the post-synaptic unit.
In some applications, a unit of the network may receive inputs from multiple input synapses (up to 10,000). A neuron dynamic process may be configured to adjust neuron parameters (e.g., excitability) based on, for example, a sum of inputs Ij received via unit's input connections as:Ī˜ΣjIj  (Eqn. 1)
As number of connections into a neuron increases, multiple spiking inputs may overwhelm the neuron process and may cause burst spiking, reduce neuron sensitivity to individual inputs, and may require manipulation of connection parameters (e.g., by using hard and or soft weight limits) in order prevent network instabilities. Accordingly, methods and apparatus are needed which, inter alia, overcome the aforementioned disabilities.